Speaker
Abstract
LLM accuracy is a challenging topic to address and is much more multi dimensional than a simple accuracy score. In this talk we’ll dive deeper into how to measure LLM related metrics, going through examples, case studies and techniques beyond just a single accuracy and score. We’ll discuss how to create, track and revise micro LLM metrics to have granular direction for improving LLM models.
Interview
I'm mainly focused on applied research and helping teams build in the LLM and conversational AI space. The goal is to look at industry challenges, create accessible and practical research and guides that help us create better conversational experiences.
Each problem in the AI space, or any use case has unique challenges. There has been a lot of focus on catch-all metrics, but once you've been serving production traffic you'll find edge cases and scenarios you want to measure. This is where micro metrics can help, defining specific outputs and behaviors that you want to track for your use case.
A range between intermediate and senior developer and product lead. The concepts are pretty standard from a product, ML and software perspective - the learning comes from thinking through the provided case studies and how they can be applied to your own use cases.
Figuring out how to prompt and steer multi model models. Speech to speech is very exciting, but businesses need the ability to check for hallucinations and integrate with other services before responding to users.
Topics
QCon San Francisco 2024 is a three day conference for senior software engineers, architects and team leads. An international program committee of working engineers selects every session. Patterns and practices, not products and pitches.
Part of the track
Generative AI in Production & Advancements Hosted by Hien Luu AI/ML Leader, Advisor, Speaker, and AuthorFrom the same track
Tuesday 19 November
10:35 Ballroom BC Session Scaling Large Language Model Serving Infrastructure at Meta Ye (Charlotte) Qi Senior Staff Engineer @Meta Running LLMs requires significant computational power, which scales with model size and context length. We will discuss strategies for fitting models to various hardware configurations and share techniques for optimizing inference latency and throughput at Meta. 11:45 Ballroom BC Session Generative AI GenAI for Productivity Mandy Gu Senior Software Development Manager @Wealthsimple At Wealthsimple, we leverage Generative AI internally to improve operational efficiency and streamline monotonous tasks. Our GenAI stack is a blend of tools we developed in house and third party solutions. 13:35 Pacific DEKJ Session LLMOps Navigating LLM Deployment: Tips, Tricks, and Techniques Meryem Arik Co-Founder and CEO @Doubleword (Previously TitanML), Recognized as a Technology Leader in Forbes 30 Under 30, Recovering Physicist Self-hosted Language Models are going to power the next generation of applications in critical industries like financial services, healthcare, and defense. 14:45 Seacliff ABC Session AI/ML Search: from Linear to Multiverse Faye Zhang Staff Software Engineer @Pinterest, Tech Lead on GenAI Search Traffic Projects, Speaker, Expert in AI/ML with a Strong Background in Large Distributed System The future of search is undergoing a revolutionary transformation, shifting from traditional linear queries to a rich multiverse of possibilities powered by AI. 15:55 Seacliff ABC Session AI/ML 10 Reasons Your Multi-Agent Workflows Fail and What You Can Do About It Victor Dibia Principal Research Software Engineer @Microsoft Research, Core Contributor to AutoGen, Author of "Multi-Agent Systems with AutoGen" book. Previously @Cloudera, @IBMResearch Multi-agent systems – a setup where multiple agents (generative AI models with access to tools) collaborate to solve complex tasks – are an emerging paradigm for building applications. 17:05 Ballroom BC Session Machine Learning A Framework for Building Micro Metrics for LLM System Evaluation Denys Linkov Head of ML @Voiceflow, LinkedIn Learning Instructor, ML Advisor and Instructor, Previously @LinkedIn LLM accuracy is a challenging topic to address and is much more multi dimensional than a simple accuracy score. In this talk we’ll dive deeper into how to measure LLM related metrics, going through examples, case studies and techniques beyond just a single accuracy and score.